Genetic Algorithms as Global Random Search Methods: An Alternative Perspective

1995 ◽  
Vol 3 (1) ◽  
pp. 39-80 ◽  
Author(s):  
Charles C. Peck ◽  
Atam P. Dhawan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Random Search ◽  
Schema Theory ◽  
Global Optimum ◽  
Global Search ◽  
Search Process ◽  
Sampling Distributions ◽  
Alternative Perspective ◽  
Global Random Search

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Global search in single-solution-based metaheuristics

2020 ◽  
Vol 54 (3) ◽  
pp. 275-296 ◽  
Author(s):  
Najmeh Sadat Jaddi ◽  
Salwani Abdullah
Keyword(s):  
Search Space ◽  
Population Based ◽  
Global Optimum ◽  
Global Search ◽  
Hill Climbing ◽  
Search Process ◽  
Local Optimum ◽  
Content Type ◽  
Solution Algorithms ◽  
Single Solution

PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Manuel Vargas ◽  
Guillermo Fuertes ◽  
Miguel Alfaro ◽  
Gustavo Gatica ◽  
Sebastian Gutierrez ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Genetic Algorithms ◽  
Global Optimum ◽  
Natural Phenomena ◽  
Mathematical Functions ◽  
Dynamic Complexity ◽  
Modified Genetic Algorithm

The dynamic complexity of time series of natural phenomena allowed to improve the performance of the genetic algorithm to optimize the test mathematical functions. The initial populations of stochastic origin of the genetic algorithm were replaced using the series of time of winds and earthquakes. The determinism of the time series brings in more information in the search of the global optimum of the functions, achieving reductions of time and an improvement of the results. The information of the initial populations was measured using the entropy of Shannon and allowed to establish the importance of the entropy in the initial populations and its relation with getting better results. This research establishes a new methodology for using determinism time series to search the best performance of the models of optimization of genetic algorithms (GA).

Deep Boltzmann Machines Aided Design Based on Genetic Algorithms

2014 ◽  
Vol 568-570 ◽  
pp. 848-851 ◽  
Author(s):  
Kai Liu ◽  
Li Min Zhang ◽  
Yong Wei Sun
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Global Search ◽  
Evaluation Function ◽  
Restricted Boltzmann Machines ◽  
Generalization Performance ◽  
Boltzmann Machines ◽  
Deep Boltzmann Machines ◽  
Aided Design

To resolve the problem of no guidance about how to set the values of numerical meta-parameters and difficulty to achieve optimization of Deep Boltzmann Machines, genetic algorithms are used to develop an automatic optimizing method named GA-RBMs (Genetic Algorithm-Restricted Boltzmann Machines) for this model’s aided design. Based on the Restricted Boltzmann Machines’ features and evaluation function, a genetic algorithm is designed and realizes the global search of satisfied structure. We also initialize the network’s weights to determine the number of visible units and hidden units. The experiments were conducted on MNIST digits handwritten datasets. The results proved that this optimization reduced the dimension of visible units and improved the performance of feature extracted by Deep Boltzmann Machines. The network optimized has good generalization performance and meets the demand of Deep Boltzmann Machines’ aided design.

scholarly journals Designing mixed metal halide ammines for ammonia storage using density functional theory and genetic algorithms

2014 ◽  
Vol 16 (36) ◽  
pp. 19732-19740 ◽  
Author(s):  
Peter Bjerre Jensen ◽  
Steen Lysgaard ◽  
Ulrich J. Quaade ◽  
Tejs Vegge
Keyword(s):  
Genetic Algorithm ◽  
Density Functional Theory ◽  
Genetic Algorithms ◽  
Density Functional ◽  
Global Optimum ◽  
Metal Halide ◽  
Functional Theory ◽  
Mixed Metal ◽  
Computational Screening ◽  
Ammonia Storage

New superior ammonia storage materials are suggested from computational screening. Global optimum of 27 000 mixtures identified testing only ∼1.5% of the candidates, proving the success of the genetic algorithm.

The Simulation and Analyses of Timing-Optimal Trajectory Planning for 6DOF Robot

2014 ◽  
Vol 716-717 ◽  
pp. 1555-1558
Author(s):  
Zhi Jian Gou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Global Search ◽  
Search Performance ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Optimal Trajectory Planning

The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained and simulated by the improved genetic algorithms under the kinematical constraints.

New Hardware Engine for New Genetic Algorithms

2012 ◽  
Vol 490-495 ◽  
pp. 1831-1838
Author(s):  
Fariborz Ahmadi ◽  
Reza Tati
Keyword(s):  
Genetic Algorithm ◽  
Genetic Diversity ◽  
Genetic Algorithms ◽  
Hardware Implementation ◽  
Global Optimum ◽  
Local Optimum ◽  
Genetic Operators ◽  
Soft Computing Method ◽  
Software Implementations ◽  
Computing Method

Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn’t produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only don’t this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones.

A Hybrid Biomimetic Genetic Algorithm Using a Local Fuzzy Simplex Search

2010 ◽  
Author(s):  
George S. Ladkany ◽  
Mohamed B. Trabia
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Rate Of Convergence ◽  
Genetic Information ◽  
Hybrid Genetic Algorithm ◽  
Global Optimum ◽  
Benchmark Problems ◽  
Simplex Search ◽  
Successful Approach ◽  
Comparison Of The Results

This paper presents a hybrid genetic algorithm that expands upon the previously successful approach of twinkling genetic algorithm (TGA) by incorporating a highly efficient local fuzzy-simplex search within the algorithm. The TGA was in principle a bio-mimetic algorithm that introduced a controlled deviation from a typical GA method, by not requiring that every genevariable of an offspring be the result of a crossover. Instead, twinkling allowed the genetic information of the randomly chosen gene locations to be directly passed on from one parent, which was shown to increase the likelihood of survival of a successful gene value within the offspring, rather than requiring it to be blended. The twinkling genetic algorithms proved highly effective at locating exact global optimum with a competitive rate of convergence for a wide variety of benchmark problems. In this work, it is proposed to couple the TGA with a fuzzy simplex local search to increase the rate of convergence of the algorithm. The proposed algorithm is tested using common mathematical and engineering design benchmark problems. Comparison of the results of this algorithm with earlier algorithms is presented.

A Hybrid Genetic Algorithm for Mixed-Discrete Design Optimization

2004 ◽  
Vol 127 (6) ◽  
pp. 1100-1112 ◽  
Author(s):  
Singiresu S. Rao ◽  
Ying Xiong
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Mechanical Design ◽  
Random Search ◽  
Hybrid Genetic Algorithm ◽  
Global Optimum ◽  
Feasible Region ◽  
Search Methods ◽  
Nonlinear Design ◽  
Optimum Solution

A new hybrid genetic algorithm is presented for the solution of mixed-discrete nonlinear design optimization. In this approach, the genetic algorithm (GA) is used mainly to determine the optimal feasible region that contains the global optimum point, and the hybrid negative subgradient method integrated with discrete one-dimensional search is subsequently used to replace the GA to find the final optimum solution. The hybrid genetic algorithm, combining the advantages of random search and deterministic search methods, can improve the convergence speed and computational efficiency compared with some other GAs or random search methods. Several practical examples of mechanical design are tested using the computer program developed. The numerical results demonstrate the effectiveness and robustness of the proposed approach.

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